Bibliographic record
Abstract
PURPOSE OF REVIEW: The majority of problems in interpreting gastritis remain Helicobacter related, but their nature has changed. The present review covers gastritis historically through cancer risk staging systems. RECENT FINDINGS: Key points to remember are: Helicobacter is associated with several forms of gastritis; in the present review, I am focusing on the two ends of the disease, 'Helicobacter pylori infection', that starts with antral predominant gastritis but can continue to oxyntic predominant disease with atrophy; the role Helicobacter pylori plays in autoimmune gastritis with pernicious anemia remains unresolved; gastritis staging systems for cancer risk, namely Baylor and Operative Link on Gastritis Assessment, are currently available. SUMMARY: As most gastric carcinomas arise on a background of atrophic gastritis, and the risk increases with the extent of atrophy, an index of atrophy location and extent could be useful in predicting patients at greatest risk for carcinoma. It is now possible to stage patients for cancer risk. Nonetheless, in a field such as gastritis in which many issues remain unresolved, a classification or staging system that is more descriptive will likely prove more useful.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".